Big data analysis has revolutionized many areas of modern life—from healthcare to politics to sports. The insurance sector is yet to see its impact. Data analytics is no longer restricted to the realm of technology in insurance. Today it is a business imperative. While providing solutions to long-standing business challenges, big data and analytics offer support in the fight against fraud. They present new opportunities of fulfilling customer needs, aptly combining the right pricing, responsive risk management systems, appropriate underwriting and accurate claims. From improving business processes and aiding penetration into newer markets, to establishing long-term, credible relationships with customers, the rewarding possibilities they offer are many.
Big data and analytics pave the way for predictive intelligence that uses algorithms to anticipate the intent of the customer. This can help closely observe customer behavior and build a profile of customer preferences, dynamically. Based on this observation, unique recommendations can be proposed.
The high volume of data and the accompanying risk of its secure usage is higher than ever. Needless to say therefore, a sound information management system is the backbone of successful deployment of big data analytics. It comprises the data itself; an appropriate IT infrastructure; a host of analytics tools; and a comprehensive analytical data mart auto-updated cyclically to contain relevant information pertaining to customers, products or transactions in a given period of time.
The Current Situation
There is immense scope for big data and analytics in the insurance domain in building better cost and operational efficiencies, while improving the overall customer experience. This is currently challenged due to limited interactions between insurers and customers. There are valid concerns around privacy of sensitive information relating to health, lifestyle and behavioral information of customers. Advanced technologies such as cloud computing masked data, and encryptions can ensure robust data privacy in a cost-effective manner, and build more trust and confidence in customers relating to the integrity of their information held by the insurer.
Mobile, which has now taken over web, necessitates an engagement model specifically devised for a digital world. Overall, consumers are far less satisfied with their experience in digital insurance than with that in other industries (see figure 1). This is especially true when it comes to ‘moments of truth’ such as paying claims.
Consumers have significant unmet needs, with many products perceived to be expensive and inflexible.
Often, insurance companies experience dissonance of expectations among customers. With more industries offering an intuitive customer experience across various digital platforms, it is increasingly necessary for the insurance industry also to be attuned to customer needs.
We see such innovations in other domains which have scope of high engagement levels with their customers, such as banking, FMCG, etc. This said, evolving digital capabilities—particularly mobile, social media, big-data, and cloud technologies—could open up avenues to offer well-timed and ingenious services through understanding, serving, and engaging customers.
Big Data Analytics: Bringing Customer to the Heart of the Insurance Value Chain
It is increasingly important to track, attend to and anticipate consumer expectations and behavior as closely as possible. With customer expectations dramatically changing, a fixed approach to products and distribution channels will soon be archaic. So far, consumers have engaged less with insurers than with any other industry* (see figure 2). Thus in comparison, customer experience with insurers has trailed behind that with other industries.
The digitalization push is getting more and more customers online for management of insurance-related transactions. In line with this phenomenon, a new environment that will influence every area of the insurance industry value chain is in order. In this evolved ecosystem, possibilities afforded by the big data approach will be actively exploited by insurance players aspiring for the leadership position.
Cross-selling capabilities: As acquiring new customers becomes increasingly expensive, the strategic focus is now on cross-selling new products to existing customers using the propensity-to-buy model.
Predictive analytics can be used to identify profitable customers and lengthen the company’s relationship with them by cross-selling other products or services to them. It can segment the existing customer base of the insurer using contact center, demographic, transactional and external marketing/risk-related data. Suitable marketing and customer communications can be tailored to reach out directly to niches thus created in the most lucrative manner. This creates a capability to subtly bundle intuitive products which may be of value to these customers.
Predictive underwriting: Advanced underwriting analytics allows insurers to have a predictive view of risks, given the need for accurate pricing of insurance products for sustained competitive advantage, with faster turnaround time. This could stretch an insurer’s ability to underwrite new risks that could earlier not be covered profitably, by studying ‘triggers’ of health or other relevant lifestyle-related data. Advanced underwriting will enable insurers to charge customers as per their lifecycle/lifestyle, as against conventional underwriting systems. This could translate into extended relationships between an insurer and its customers.
Claims and fraud management: Predictive analytics can help determine whether a claim intimation needs further investigation. It will also help in determining the complexity of the claim, accurately. This expedites processing of legitimate claims, leading to enhanced customer satisfaction, while simultaneously deterring payouts for fraudulent claims.
Insurers dedicated to fighting fraud will be able to send a strong message to fraudsters and enhance their image in the eyes of genuine customers. This also does away with introduction of cautionary processes which leaves a blot on the experience of a majority of customers.
Persistency and surrender: In a setting wherein insurance is already being pushed and sold and not bought willingly, lapse or surrender situations will impact not just the insurer’s persistency ratio as a firm, but also customers’ protection against future risks. In the latter situation, the surrender value needs to be paid out to the customer, which in turn could impact all the stakeholders involved such as the customer, distributors etc.
Through propensity modeling techniques, insurance companies can predict the likelihood of a customer lapsing or surrendering a policy. This will enable insurers to monitor relevant customer sets and in turn, influence them to stay committed to their investment for the longer haul and maximize their benefits.
Better engagement and servicing: Insurance customers the world over now look forward to a different digital experience. This fact constitutes one of the key insights from a global consumer survey carried out in 12 countries, as part of a 2014 study by Morgan Stanley and BCG. People are now relying on social media to research purchases. It is also the gateway through which consumer buying happens. Roughly 50 percent of respondents from India and China claimed to count on social media posts by friends and family as a crucial source of information for their insurance choice, versus 16 percent and 18 percent in the UK and Germany respectively. Dependence on social media is likely to grow further, indicating the need for insurance firms to develop a well-defined strategy on managing social media.
Insurance companies gather huge volumes of text through various touch-points such as agents, contact centers, blogs, emails and social networks. The information collected includes policies, expert and health reports, claims, complaints, results of surveys, relevant interactions between customers and non-customers in social networks, etc. Insurance players are among those who could benefit most through intelligent analysis of free text (text analytics), wherein interactions could be categorized according to the product or service offered, the marketing channel used, type of interaction, resolution status, etc. As part of this, sentiment analysis and automatic opinion techniques help identify the polarity of the sentiment (positive, negative or neutral) towards specific aspects of a product, process or channel.
Analysis of information across multiple channels will be used in combination with hypothesis-driven analytics to develop and tailor personalized products, services, delivery methods and communications. Superior consumer experience drives valued cross-selling and persistency improvements. Companies can achieve this through a combination of consumer-centric design, branding, and social media engagement.
With these capabilities, insurers will also model and test new products regularly, inventively and seamlessly; be it region-wise, as per specific customer cohorts, or specific time scales, to generate newer insights. Thus, a truly user-friendly and integrated experience across channels will be offered to customers, while ensuring higher value for money for the organization.
To a large extent, this level of customer-centricity calls for changes at the structural level of an organization, wherein focus on the customer is at the heart of the firm’s value system. This implies a re-haul in orientation from selling products to delivering in line with customers’ needs.
Ms RM Vishakha, Managing Director & CEO, IndiaFirst Life Insurance Company Li
mited writes this piece for FICCI BCG Publication “The Changing Face of Indian Insurance”.
